ISSN 0253-2778

CN 34-1054/N

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Sparse-Lagrangian FDF simulation of Sandia flame E with modified density coupling

  • Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.05.006
  • Received Date: 22 March 2019
  • Accepted Date: 10 May 2019
  • Rev Recd Date: 10 May 2019
  • Publish Date: 31 May 2020
    Abstract

  • Abstract

    Sparse-Lagrangian filtered density function (FDF) simulation is performed for Sandia flame E. Sparse-Lagrangian Monte Carlo method is used to solve the scalar FDF transport equations, in which a generalized multiple mapping conditioning (MMC) model is implemented to enforce mixing localness. A modified density coupling method for Sparse-Lagrangian FDF is proposed, in which the conditional mean source term of equivalent enthalpy is fed back directly as the source term in the Eulerian equivalent enthalpy transport equation of large eddy simulation (LES). The results of Sparse-Lagrangian FDF simulation indicated that the new density coupling method is more reasonable than the previous ones in reducing the numerical errors in LES results, and that the simulation results agree well with the experimental data.

    Abstract

    Sparse-Lagrangian filtered density function (FDF) simulation is performed for Sandia flame E. Sparse-Lagrangian Monte Carlo method is used to solve the scalar FDF transport equations, in which a generalized multiple mapping conditioning (MMC) model is implemented to enforce mixing localness. A modified density coupling method for Sparse-Lagrangian FDF is proposed, in which the conditional mean source term of equivalent enthalpy is fed back directly as the source term in the Eulerian equivalent enthalpy transport equation of large eddy simulation (LES). The results of Sparse-Lagrangian FDF simulation indicated that the new density coupling method is more reasonable than the previous ones in reducing the numerical errors in LES results, and that the simulation results agree well with the experimental data.
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    • References(25)
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    WANDEL A P, LINDSTEDT R P. Hybrid multiple mapping conditioning modeling of local extinction[J] . Proceedings of the Combustion Institute, 2013, 34: 1365-1372.
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    VO S, STEIN O T, KRONENBURG A, et al. Assessment of mixing time scales for a sparse particle method[J] . Combustion and Flame, 2017, 179: 280-299.
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    GE Y, CLEARY M J, KLIMENKO A Y. Sparse-lagrangian FDF simulations of sandia flame E with density coupling[J] . Proceedings of the Combustion Institute, 2011, 33: 1401-1409.
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    RAMAN V, PITSCH H. A consistent LES/filtered-density function formulation for the simulation of turbulent flames with detailed chemistry[J] . Proceedings of the Combustion Institute, 2007, 31: 1711-1719.
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    RAMAN V, PITSCH H. Hybrid large-eddy simulation/Lagrangian filtered-density-function approach for simulating turbulent combustion[J] . Combustion and Flame, 2005, 143: 56-78.
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    POPOV P P, WANG H, POPE S P. Specific volume coupling and convergence properties in hybrid particle/finite volume algorithms for turbulent reactive flows[J]. Journal of Computational Physics, 2015, 294: 110-126.
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    BARLOW R S, FRANK J H. Effects of turbulence on species mass fractions in methane/air jet flames[J] . Symposium (International) on Combustion, 1998, 27: 1087-1095.
    [24]
    ANKARAN R S, HAWKES E R, CHEN J H, et al. Structure of a spatially developing turbulent lean methane-air Bunsen flame[J] . Proceedings of the Combustion Institute, 2007, 31: 1291-1298.
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    BROWN P N, BYRNE G D, HINDMARSH A C. VODE: a variable coefficient ODE solver[J] . SIAM Journal on Scientific and Statistical Computing, 1989, 10: 1038-1051.
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    [1]
    SUBRAMANIAM S, POPE S B. A mixing model for turbulent reactive flows based on Euclidean minimum spanning trees[J] . Combustion and Flame, 1998, 115: 487-514.
    [2]
    KLIMENKO A Y, POPE S B. The modeling of turbulent reactive flows based on multiple mapping conditioning[J] . Physics of Fluids, 2003, 15: 1907-1925.
    [3]
    POPE S B. A model for turbulent mixing based on shadow-position conditioning[J] . Physics of Fluids, 2013, 25: 110803.
    [4]
    GALINDO S, SALEHI F, CLEARY M J, et al. MMC-LES simulations of turbulent piloted flames with varying levels of inlet inhomogeneity[J] . Proceedings of the Combustion Institute, 2017, 36: 1759-1766.
    [5]
    SUNDARAM B, KLIMENKO A Y, CLEARY M J, et al. A direct approach to generalised multiple mapping conditioning for selected turbulent diffusion flame cases[J] . Combustion Theory and Modelling, 2016, 20(4): 735-764.
    [6]
    DEVAUD C B, STANKOVIC I, MERCI B. Deterministic Multiple Mapping Conditioning (MMC) applied to a turbulent flame in Large Eddy Simulation (LES)[J] . Proceedings of the Combustion Institute, 2013, 34: 1213-1221.
    [7]
    WANDEL A P, LINDSTEDT R P. Hybrid multiple mapping conditioning modeling of local extinction[J] . Proceedings of the Combustion Institute, 2013, 34: 1365-1372.
    [8]
    SUNDARAM B, KLIMENKO A Y, CLEARY M J, et al. Prediction of NOx in premixed high-pressure lean methane flames with a MMC-partially stirred reactor[J] . Proceedings of the Combustion Institute, 2015, 35: 1517-1525.
    [9]
    SHETTY A, CHANDY A J, FRANKEL S H. A new fractal interaction by exchange with the mean mixing model for large eddy simulation/filtered mass density function applied to a multiscalar three-stream turbulent jet[J] . Physics of Fluids, 2010, 22: 025102.
    [10]
    MCDERMOTT R, POPE S B. A particle formulation for treating differential diffusion in filtered density function methods[J] . Journal of Computational Physics, 2007, 226: 947-993.
    [11]
    MEYER D W, JENNY P. A mixing model for turbulent flows based on parameterized scalar profiles[J] . Physics of Fluids, 2006, 18: 035105.
    [12]
    JABERI F A, COLUCCI P J, JAMES S, et al. Filtered mass density function for large-eddy simulation of turbulent reacting flows[J] . Journal of Fluid Mechanics, 1999, 401: 85-121.
    [13]
    CLEARY M J, KLIMENKO A Y. A detailed quantitative analysis of sparse-lagrangian filtered density function simulations in constant and variable density reacting jet flows[J] . Physics of Fluids, 2011, 23: 115102.
    [14]
    CLEARY M J, KLIMENKO A Y, JANICKA J, et al. A sparse-lagrangian multiple mapping conditioning model for turbulent diffusion flames[J] . Proceedings of the Combustion Institute, 2009, 32: 1499-1507.
    [15]
    VO S, STEIN O T, KRONENBURG A, et al. Assessment of mixing time scales for a sparse particle method[J] . Combustion and Flame, 2017, 179: 280-299.
    [16]
    GE Y, CLEARY M J, KLIMENKO A Y. Sparse-lagrangian FDF simulations of sandia flame E with density coupling[J] . Proceedings of the Combustion Institute, 2011, 33: 1401-1409.
    [17]
    BARLOW R S, FRANK J H, KARPETIS A N, et al. Piloted methane/air jet flames: transport effects and aspects of scalar structure[J] . Combustion and Flame, 2005, 143: 433-449.
    [18]
    MENEVEAU C, LUND T S, CABOT W H. A Lagrangian dynamic subgrid-scale model of turbulence[J] . Journal of Fluid Mechanics, 1996, 319: 353-385.
    [19]
    MURADOGLU M, POPE S P, CAUGHEY D A. The hybrid method for the PDF equations of turbulent reactive flows: consistency conditions and correction algorithms[J] . Journal of Computational Physics, 2001, 172: 841-878.
    [20]
    RAMAN V, PITSCH H. A consistent LES/filtered-density function formulation for the simulation of turbulent flames with detailed chemistry[J] . Proceedings of the Combustion Institute, 2007, 31: 1711-1719.
    [21]
    RAMAN V, PITSCH H. Hybrid large-eddy simulation/Lagrangian filtered-density-function approach for simulating turbulent combustion[J] . Combustion and Flame, 2005, 143: 56-78.
    [22]
    POPOV P P, WANG H, POPE S P. Specific volume coupling and convergence properties in hybrid particle/finite volume algorithms for turbulent reactive flows[J]. Journal of Computational Physics, 2015, 294: 110-126.
    [23]
    BARLOW R S, FRANK J H. Effects of turbulence on species mass fractions in methane/air jet flames[J] . Symposium (International) on Combustion, 1998, 27: 1087-1095.
    [24]
    ANKARAN R S, HAWKES E R, CHEN J H, et al. Structure of a spatially developing turbulent lean methane-air Bunsen flame[J] . Proceedings of the Combustion Institute, 2007, 31: 1291-1298.
    [25]
    BROWN P N, BYRNE G D, HINDMARSH A C. VODE: a variable coefficient ODE solver[J] . SIAM Journal on Scientific and Statistical Computing, 1989, 10: 1038-1051.
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